Papers with Code 아카이브
3D Face Reconstruction
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3D Face Reconstruction is a computer vision task that involves creating a 3D model of a human face from a 2D image or a set of images. The goal of 3D face reconstruction is to reconstruct a digital 3D representation of a person's face, which can be used for various applications such as animation, virtual reality, and biometric identification.
<span style="color:grey; opacity: 0.6">( Image credit: 3DDFA_V2 )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/lkeonwoo94/DL_cv-mdt_NVIDIA_Cert_Course_StudyPI
https://github.com/llSeedll/Facial-Recognition-using-Facenet
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/llsouder/screen-ocr
https://github.com/longchr123/insightface-analysis
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/longnguyen2/blazeface
BlazeFace: Sub-millisecond Neural Face Detection on Mobile GPUs
https://github.com/lonlyness/pggan-structure
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/lonlyness/variational_temporal_abstraction
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/lornatang/googlenet-pytorch
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/ls-da3m0ns/Short-ResNet
https://github.com/lsaiml/CaVINet
Deep Cross Modal Learning for Caricature Verification and Identification(CaVINet)
https://github.com/lucasleesw/PCANet_python_ver
PCANet: A Simple Deep Learning Baseline for Image Classification?
https://github.com/lucasxlu/ComboLoss
SCUT-FBP5500: A Diverse Benchmark Dataset for Multi-Paradigm Facial Beauty Prediction
https://github.com/luciennnnnnn/dualformer
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/luckycallor/insightface-tensorflow
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/lucylow/salty-wet-man
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/lxy000719/ResNet-50-pneumonia-classification
https://github.com/lyatdawn/lightcnn-mxnet
https://github.com/lyk19940625/MyRFBNet
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/lyp-deeplearning/awesome-retinal-registration
https://github.com/lzx551402/ASLFeat
Circle Loss: A Unified Perspective of Pair Similarity Optimization
https://github.com/m-117/PointNet-ein-Implementationsbeispiel-mit-Jupyter-Notebooks
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/m-kahla/Label-Only-Model-Inversion-Attacks-via-Boundary-Repulsion
https://github.com/ma921/XRDidentifier
AdaCos: Adaptively Scaling Cosine Logits for Effectively Learning Deep Face Representations
https://github.com/ma9o/facematch
https://github.com/mabhay3420/Deep-Into-CNN
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/machanic/AU_R-CNN
AU R-CNN: Encoding Expert Prior Knowledge into R-CNN for Action Unit Detection
https://github.com/maciejdomagala/DLWP_notes
https://github.com/mackitron/COVID19
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/madhavambati/Face-Recognition
https://github.com/maggie0106/Graph-CNN-in-3D-Point-Cloud-Classification
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/magicsunday/pico
Object Detection with Pixel Intensity Comparisons Organized in Decision Trees
https://github.com/magnificent1208/r3det-on-mmdetection
https://github.com/mahathi26/ciss
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/mahmoodlab/hest
https://github.com/mahxn0/RetinaFace_For_Face_Plate_Detect
RetinaFace: Single-stage Dense Face Localisation in the Wild
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.